Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Language-Based Bayesian Optimization Research Assistant (BORA)
Many important scientific problems involve multivariate optimization coupled with slow and laborious experimental measurements. These complex, high-dimensional searches can be defined by non-convex optimization landscape…
Bayesian OptimizationBoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives
Scientific optimization problems are usually concerned with balancing multiple competing objectives, which come as preferences over both the outcomes of an experiment (e.g. maximize the reaction yield) and the correspond…
Bayesian OptimizationCausal Discovery via Bayesian Optimization
Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG…
Bayesian OptimizationCausal DiscoveryGaussian ProcessesSafety in safe Bayesian optimization and its ramifications for control
A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only through noisy evaluations. For example, in co…
Bayesian OptimizationLITE: Efficiently Estimating Gaussian Probability of Maximality
We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challenge in applications ranging from Bayesian …
Bayesian OptimizationDrug DiscoveryCo-Learning Bayesian Optimization
Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such …
AttributeBayesian OptimizationDiversityglobal-optimizationSample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
Modern machine learning algorithms, especially deep learning based techniques, typically involve careful hyperparameter tuning to achieve the best performance. Despite the surge of intense interest in practical technique…
Bayesian OptimizationDeep LearningSelecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization
We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility in…
Bayesian OptimizationLipschitz Safe Bayesian Optimization for Automotive Control
Controller tuning is a labor-intensive process that requires human intervention and expert knowledge. Bayesian optimization has been applied successfully in different fields to automate this process. However, when tuning…
Bayesian OptimizationDistributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization
This paper introduces a new approach that leverages Multi-agent Bayesian Optimization (MABO) to design Distributed Model Predictive Control (DMPC) schemes for multi-agent systems. The primary objective is to learn optima…
Bayesian OptimizationDistributed OptimizationModel Predictive ControlInformation-theoretic Bayesian Optimization: Survey and Tutorial
Several scenarios require the optimization of non-convex black-box functions, that are noisy expensive to evaluate functions with unknown analytical expression, whose gradients are hence not accessible. For example, the …
Bayesian OptimizationSurveyEarly Stopping Bayesian Optimization for Controller Tuning
Manual tuning of performance-critical controller parameters can be tedious and sub-optimal. Bayesian Optimization (BO) is an increasingly popular practical alternative to automatically optimize controller parameters from…
Bayesian OptimizationDeepEyeNet: Adaptive Genetic Bayesian Algorithm Based Hybrid ConvNeXtTiny Framework For Multi-Feature Glaucoma Eye Diagnosis
Glaucoma is a leading cause of irreversible blindness worldwide, emphasizing the critical need for early detection and intervention. In this paper, we present DeepEyeNet, a novel and comprehensive framework for automated…
Bayesian OptimizationQGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecasting
Accurate weather forecasting holds significant importance, serving as a crucial tool for decision-making in various industrial sectors. The limitations of statistical models, assuming independence among data points, high…
Bayesian OptimizationDecision MakingWeather ForecastingWhich price to pay? Auto-tuning building MPC controller for optimal economic cost
Model predictive control (MPC) controller is considered for temperature management in buildings but its performance heavily depends on hyperparameters. Consequently, MPC necessitates meticulous hyperparameter tuning to a…
Bayesian OptimizationComputational EfficiencyHyperparameter OptimizationModel Predictive ControlMathematical model of parameters relevance in adaptive level-crossing sampling for electrocardiogram signals
Digital acquisition of bio-signals has been mostly dominated by uniform time sampling following the Nyquist theorem. However, in recent years, new approaches have emerged, focused on sampling a signal only when certain e…
Bayesian OptimizationData CompressionOn the convergence rate of noisy Bayesian Optimization with Expected Improvement
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open questions remain on the theoretical con…
Bayesian OptimizationDerivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process
Gaussian process (GP) is arguably one of the most widely used machine learning algorithms in practice. One of its prominent applications is Bayesian optimization (BO). Although the vanilla GP itself is already a powerful…
Bayesian OptimizationOptimizing Distributed Deployment of Mixture-of-Experts Model Inference in Serverless Computing
With the advancement of serverless computing, running machine learning (ML) inference services over a serverless platform has been advocated, given its labor-free scalability and cost effectiveness. Mixture-of-Experts (M…
Bayesian OptimizationCPUMixture-of-ExpertsModeling All Response Surfaces in One for Conditional Search Spaces
Bayesian Optimization (BO) is a sample-efficient black-box optimizer commonly used in search spaces where hyperparameters are independent. However, in many practical AutoML scenarios, there will be dependencies among hyp…
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